Absence of Cycles in Symmetric Neural Networks

Authors

Xin Wang, University of California, Los Angeles
Arun Jagota, University of California, Santa CruzFollow
Fernanda Botelho, University of MemphisFollow
Max Garzon, University of Memphis

Abstract

For a given recurrent neural network, a discrete-time model may have asymptotic dynamics different from the one of a related continuous-time model. In this article, we consider a discrete-time model that discretizes the continuous-time leaky integrator model and study its parallel, sequential, block-sequential, and distributed dynamics for symmetric networks. We provide sufficient (and in many cases necessary) conditions for the discretized model to have the same cycle-free dynamics of the corresponding continuous-time model in symmetric networks.

Publication Title

Neural Computation

Recommended Citation

Wang, X., Jagota, A., Botelho, F., & Garzon, M. (1998). Absence of Cycles in Symmetric Neural Networks. Neural Computation, 10 (5), 1235-1249. https://doi.org/10.1162/089976698300017430